This document discusses the role and importance of statistics in scientific research. It begins by defining statistics as the science of learning from data and communicating uncertainty. Statistics are important for summarizing, analyzing, and drawing inferences from data in research studies. They also allow researchers to effectively present their findings and support their conclusions. The document then describes how statistics are used and are important in many fields of scientific research like biology, economics, physics, and more. It also provides examples of statistical terms commonly used in research studies and some common misuses of statistics.
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
Detailed Lesson plan on persuasive writing.pdfJohnGondran
This document outlines a lesson plan for a Grade 10 English class on persuasive writing. The lesson plan covers the objectives, topics, strategies, materials and procedures for the class. Key points include defining persuasive writing and its parts, discussing words and phrases commonly used, and having students complete an activity and assignment related to persuasive techniques. Students will work in groups to creatively demonstrate understanding of persuasive writing and will submit a written assignment applying persuasive writing to scenarios.
Introduction to statistics...ppt rahulRahul Dhaker
This document provides an introduction to statistics and biostatistics. It discusses key concepts including:
- The definitions and origins of statistics and biostatistics. Biostatistics applies statistical methods to biological and medical data.
- The four main scales of measurement: nominal, ordinal, interval, and ratio scales. Nominal scales classify data into categories while ratio scales allow for comparisons of magnitudes and ratios.
- Descriptive statistics which organize and summarize data through methods like frequency distributions, measures of central tendency, and graphs. Frequency distributions condense data into tables and charts. Measures of central tendency include the mean, median, and mode.
Good Clinical Practice Guidelines (ICH GCP E6).pptMohamed Fazil M
M. Pharmacy - Pharmaceutical Regulatory Affairs (MRA)
1st Semester - Clinical Research Regulations (MRA 103T)
Unit 4 - Clinical Research Related Guidelines: Good Clinical Practice Guidelines (ICH GCP E6)
THE PRINCIPLES OF ICH GCP
INSTITUTIONAL REVIEW BOARD/ INDEPENDENT ETHICS COMMITEE
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This presentation quotes various pharmaceutical calculations with examples. The following aspects like percentage calculations, alcoholic dilutions, Alligation method, proof spirit calculations, isotonicity adjustment, posology, temperature measurements, dialysis clearance, Pharmacokinetics calculations were covered with examples.
The document discusses communication skills and effective communication. It defines communication as the exchange of information through various senses and channels. It emphasizes that communication skills are important for careers and personal relationships. Effective communication involves sending clear, concise messages and properly understanding messages received through various verbal, nonverbal, and paraverbal means. Barriers to communication like organizational issues or personal attitudes can interfere with the exchange of information.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
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Introductory Statistics discusses the definition and history of statistics. Statistics deals with quantitative or numerical data and is the scientific method of collecting, organizing, analyzing, and making decisions with quantitative data. Historically, Indian texts from the Mauryan period and Mughal period contained early forms of statistical analysis of topics like agriculture. The typical process of a statistical study involves defining objectives, identifying the population and characteristics, planning data collection, collecting and organizing data, performing statistical analysis, and drawing conclusions. Statistics is useful for simplifying complex data, quantifying uncertainty, discovering patterns to enable forecasting, and testing assumptions. Statistical techniques have various applications in fields like marketing, economics, finance, operations, human resources, information technology,
This document discusses different types of statistics used in research. Descriptive statistics are used to organize and summarize data using tables, graphs, and measures. Inferential statistics allow inferences about populations based on samples through techniques like surveys and polls. The key difference is that descriptive statistics describe samples while inferential statistics allow conclusions about populations beyond the current data.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
The two major areas of statistics are: descriptive statistics and inferential statistics. In this presentation, the difference between the two are shown including examples.
This document introduces the concept of data classification and levels of measurement in statistics. It explains that data can be either qualitative or quantitative. Qualitative data consists of attributes and labels while quantitative data involves numerical measurements. The document also outlines the four levels of measurement - nominal, ordinal, interval, and ratio - from lowest to highest. Each level allows for different types of statistical calculations, with the ratio level permitting the most complex calculations like ratios of two values.
A power point presentation on statisticsKriace Ward
Statistics originated from Latin, Italian, and German words referring to organized states. Gottfried Achenwall is considered the "father of statistics" for coining the term to describe a specialized branch of knowledge. Modern statistics is defined as the science of judging collective phenomena through analysis and enumeration. While statistics can be an art and a science, its successful application depends on the skill of the statistician and their knowledge of the field being studied. Statistics are important across many domains from business, economics, and planning to the sciences. However, statistics also have limitations such as only studying aggregates, not individuals, and results being valid only on average and in the long run.
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
This document discusses statistics and their uses in various fields such as business, health, learning, research, social sciences, and natural resources. It provides examples of how statistics are used in starting businesses, manufacturing, marketing, and engineering. Statistics help decision-makers reduce ambiguity and assess risks. They are used to interpret data and make informed decisions. However, statistics also have limitations as they only show averages and may not apply to individuals.
This document provides an overview of time series analysis and its key components. It discusses that a time series is a set of data measured at successive times joined together by time order. The main components of a time series are trends, seasonal variations, cyclical variations, and irregular variations. Time series analysis is important for business forecasting, understanding past behavior, and facilitating comparison. There are two main mathematical models used - the additive model which assumes data is the sum of its components, and the multiplicative model which assumes data is the product of its components. Decomposition of a time series involves discovering, measuring, and isolating these different components.
This document provides an introduction to statistics. It defines statistics as the scientific methods for collecting, organizing, summarizing, presenting and analyzing data to derive valid conclusions. Statistics is useful across many fields and careers as it helps make informed decisions based on data. The document outlines descriptive and inferential statistics, and notes that descriptive statistics simplifies complexity while inferential statistics allows for conclusions to be drawn. It also discusses types of data sources, including primary data collected directly and secondary data that has already been collected.
This document provides an introduction to statistics, including definitions, types, data measurement, and important terms. It defines statistics as the collection, analysis, interpretation, and presentation of numerical data. Statistics can be descriptive, dealing with conclusions about a particular group, or inferential, using a sample to make inferences about a larger population. There are four levels of data measurement - nominal, ordinal, interval, and ratio. Important statistical terms defined include population, sample, parameter, and statistic.
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
The document discusses various methods for presenting data, including tabular, visual, graphical and diagrammatical presentation. It provides guidelines for constructing effective tables, graphs, diagrams and choosing the appropriate method based on the type of data. Tables are useful for presenting exact data while graphs and diagrams make complex data easier to understand visually. The key is to present data in a clear, concise and organized manner that facilitates analysis and understanding.
Big data presents both opportunities and challenges for data scientists. It can provide valuable insights but is difficult to manage and analyze due to its huge size and complexity. This document discusses the challenges of working with big data, such as lack of skilled professionals, issues with data growth and integration, and ensuring data security and privacy. It also covers some common applications of big data in domains like transportation, finance, healthcare, education and social media. Finally, it notes that while big data analysis can provide useful results, producing real-time insights is challenging due to the time required to process large datasets.
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET
Introductory Statistics discusses the definition and history of statistics. Statistics deals with quantitative or numerical data and is the scientific method of collecting, organizing, analyzing, and making decisions with quantitative data. Historically, Indian texts from the Mauryan period and Mughal period contained early forms of statistical analysis of topics like agriculture. The typical process of a statistical study involves defining objectives, identifying the population and characteristics, planning data collection, collecting and organizing data, performing statistical analysis, and drawing conclusions. Statistics is useful for simplifying complex data, quantifying uncertainty, discovering patterns to enable forecasting, and testing assumptions. Statistical techniques have various applications in fields like marketing, economics, finance, operations, human resources, information technology,
This document discusses different types of statistics used in research. Descriptive statistics are used to organize and summarize data using tables, graphs, and measures. Inferential statistics allow inferences about populations based on samples through techniques like surveys and polls. The key difference is that descriptive statistics describe samples while inferential statistics allow conclusions about populations beyond the current data.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
The two major areas of statistics are: descriptive statistics and inferential statistics. In this presentation, the difference between the two are shown including examples.
This document introduces the concept of data classification and levels of measurement in statistics. It explains that data can be either qualitative or quantitative. Qualitative data consists of attributes and labels while quantitative data involves numerical measurements. The document also outlines the four levels of measurement - nominal, ordinal, interval, and ratio - from lowest to highest. Each level allows for different types of statistical calculations, with the ratio level permitting the most complex calculations like ratios of two values.
A power point presentation on statisticsKriace Ward
Statistics originated from Latin, Italian, and German words referring to organized states. Gottfried Achenwall is considered the "father of statistics" for coining the term to describe a specialized branch of knowledge. Modern statistics is defined as the science of judging collective phenomena through analysis and enumeration. While statistics can be an art and a science, its successful application depends on the skill of the statistician and their knowledge of the field being studied. Statistics are important across many domains from business, economics, and planning to the sciences. However, statistics also have limitations such as only studying aggregates, not individuals, and results being valid only on average and in the long run.
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
This document discusses statistics and their uses in various fields such as business, health, learning, research, social sciences, and natural resources. It provides examples of how statistics are used in starting businesses, manufacturing, marketing, and engineering. Statistics help decision-makers reduce ambiguity and assess risks. They are used to interpret data and make informed decisions. However, statistics also have limitations as they only show averages and may not apply to individuals.
This document provides an overview of time series analysis and its key components. It discusses that a time series is a set of data measured at successive times joined together by time order. The main components of a time series are trends, seasonal variations, cyclical variations, and irregular variations. Time series analysis is important for business forecasting, understanding past behavior, and facilitating comparison. There are two main mathematical models used - the additive model which assumes data is the sum of its components, and the multiplicative model which assumes data is the product of its components. Decomposition of a time series involves discovering, measuring, and isolating these different components.
This document provides an introduction to statistics. It defines statistics as the scientific methods for collecting, organizing, summarizing, presenting and analyzing data to derive valid conclusions. Statistics is useful across many fields and careers as it helps make informed decisions based on data. The document outlines descriptive and inferential statistics, and notes that descriptive statistics simplifies complexity while inferential statistics allows for conclusions to be drawn. It also discusses types of data sources, including primary data collected directly and secondary data that has already been collected.
This document provides an introduction to statistics, including definitions, types, data measurement, and important terms. It defines statistics as the collection, analysis, interpretation, and presentation of numerical data. Statistics can be descriptive, dealing with conclusions about a particular group, or inferential, using a sample to make inferences about a larger population. There are four levels of data measurement - nominal, ordinal, interval, and ratio. Important statistical terms defined include population, sample, parameter, and statistic.
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
The document discusses various methods for presenting data, including tabular, visual, graphical and diagrammatical presentation. It provides guidelines for constructing effective tables, graphs, diagrams and choosing the appropriate method based on the type of data. Tables are useful for presenting exact data while graphs and diagrams make complex data easier to understand visually. The key is to present data in a clear, concise and organized manner that facilitates analysis and understanding.
Big data presents both opportunities and challenges for data scientists. It can provide valuable insights but is difficult to manage and analyze due to its huge size and complexity. This document discusses the challenges of working with big data, such as lack of skilled professionals, issues with data growth and integration, and ensuring data security and privacy. It also covers some common applications of big data in domains like transportation, finance, healthcare, education and social media. Finally, it notes that while big data analysis can provide useful results, producing real-time insights is challenging due to the time required to process large datasets.
Data science applications can be found in many domains including business, healthcare, urban planning, and more. In business, data science is used to optimize operations and customer experiences. In healthcare, data science aims to improve efficiency, reduce readmissions, and enable earlier disease detection. For urban areas experiencing rapid growth, data science combines with urban informatics to help address challenges. Case studies show how data science is used in cancer research by leveraging large datasets and algorithms, in healthcare by Stanford and Google to advance precision medicine, in political elections through micro-targeting, and with the growing Internet of Things to analyze data from billions of connected devices.
The document discusses SMAC (Social, Mobile, Analytics, and Cloud) technologies and how they are driving enterprise innovation. It states that the four technologies work together in an ecosystem, with each technology enabling the others. This allows enterprises to leverage the cloud to store and analyze huge amounts of customer data generated over mobile devices and social media in order to gain business advantages. It also discusses trends in SMAC, areas of interest, and examples of big data analytics.
Data science applications and use cases were discussed. Examples included using data science in business for tasks like optimizing operations, healthcare to improve efficiency and care, and urban planning to address challenges in cities. Data science contrasts with other disciplines by combining technical skills from computer science, mathematics, and statistics to analyze large datasets. Case studies demonstrated data science applications in domains like cancer research using patterns in biomedical data, healthcare to power precision medicine, political campaigns using social media microtargeting, and the growing Internet of Things producing large volumes of data.
This document discusses various applications of big data across different domains. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses how big data is being used in social media for recommendation systems, marketing, electioneering and influence analysis. Applications in healthcare discussed include personalized medicine, clinical trials, electronic health records, and genomics. Uses of big data in smart cities are also summarized, such as for smart transport, traffic management, smart energy, and smart governance. Specific examples and case studies are provided to illustrate the benefits and savings achieved from leveraging big data across these various sectors.
Data science applications and use cases were discussed. Examples included using data science in business for tasks like car design and insurance, in healthcare for reducing readmissions and improving care, and in urban planning to address challenges in growing cities. Cancer research was highlighted as an area using big data analytics and machine learning to identify patterns linked to cancer. Healthcare examples included using genetic data at Stanford Medicine for precision health. Data science was applied to political elections through Obama's targeted social media campaigns. Finally, the growing field of internet of things was noted as an area that will produce huge volumes of data for analysis.
Data mining involves extracting patterns from large data sets. It is used to uncover hidden information and relationships within data repositories like databases, text files, social networks, and computer simulations. The patterns discovered can be used by organizations to make better business decisions. Some common applications of data mining include credit card fraud detection, customer segmentation for marketing, and scientific research. The process involves data preparation, algorithm selection, model building, and interpretation. While useful, data mining also raises privacy, security, and ethical concerns if misused.
Data mining involves extracting patterns from large data sets. It is used to uncover hidden information and relationships within data repositories like databases, text files, social networks, and computer simulations. The patterns discovered can be used by organizations to make better business decisions. Some common applications of data mining include credit card fraud detection, customer segmentation for marketing, and network intrusion detection. The data mining process involves data preparation, algorithm selection, model building, and pattern evaluation.
This document provides an overview of data science. It defines data as facts such as numbers, words, measurements, and descriptions. Data science involves developing methods to analyze and extract useful insights from both structured and unstructured data. While data mining focuses on analyzing large datasets, data science covers the entire data lifecycle. There is a growing demand for data scientists as every industry relies on data. Data scientists use various statistical techniques to find patterns in data and gain knowledge. Netflix is used as a case study to show how it has become a data-driven business that uses data science to power recommendations and improve the customer experience.
This document discusses big data in nursing and education. It defines big data as large datasets that are too large and complex for traditional database systems to analyze. Some key points:
- Florence Nightingale was an early adopter of data analytics to study mortality rates in the Crimean War.
- Big data has 5 characteristics - volume, velocity, variety, veracity, and value. It is collected from a variety of sources like social media, sensors, videos.
- Big data can benefit education by addressing inequities, providing personalized learning based on student profiles, and improving student outcomes through predictive analytics.
- Challenges to big data use include technical issues in handling large datasets, privacy and ethics concerns,
Big data has the potential to transform nursing education and healthcare. It allows analysis of large, diverse datasets to reveal patterns and trends. Nursing has a long history of using data to improve patient care. Now, with big data and analytics, insights can be gained from vast amounts of structured and unstructured data from various sources. This can help personalize learning and predict outcomes. However, challenges include technical issues, privacy concerns, and developing a data-driven culture. With collaboration across sectors and letting the data speak, big data can advance nursing knowledge and the learning healthcare system.
The document discusses how healthcare organizations are increasingly using data analytics and data science. It notes that healthcare analytics aims to improve clinical care while reducing costs. Examples are given of organizations that are using analytics, such as predicting patient admissions and using inhaler trackers to identify asthma trends. The document also discusses challenges like privacy concerns with data and establishing standards. It predicts future trends will include more wearable devices generating health data and increased data sharing through electronic records.
Application of probability in daily life and in civil engineeringEngr Habib ur Rehman
This document provides examples of how statistics are used in various fields including civil engineering, real life situations, and the sciences. It discusses how statistics are applied in areas like weather forecasting, emergency preparedness, psychology, the stock market, predicting disease, education, genetics, political campaigns, quality testing, banking, business, insurance, consumer goods, management, medical studies, large companies, and the natural and social sciences. It also provides specific examples of applying statistics in civil engineering domains such as sanitary engineering, traffic engineering, surveying, coastal/port engineering, geotechnical engineering, hydrology, environmental engineering, earthquake engineering, and structural engineering.
Data Con LA 2019 - Innovating with Data by Joey BeitdashtooData Con LA
There are three classes of business models for leveraging data:1) Data as a Competitive Advantage (use of Data, advanced analytics and data science capabilities create competitive advantage.)2) Data as Improvement of Existing products or services 9Use of data into exiting offerings, effectively differentiating the value in the market.)3) Data as the Product (this class of business is a step beyond utilizing data for competitive advantage or plugging data into existing products.) Data becomes the asset or product to be monetized.)
This document discusses the many uses of statistics in civil engineering and in daily life. It begins by defining statistics and then provides examples of how statistics are used in weather forecasting, emergency preparedness, psychology, the stock market, predicting disease, education, genetics, political campaigns, quality testing, banking, business management, insurance, consumer goods, government administration, medical studies, large companies, natural and social sciences, astronomy, sanitary engineering, traffic engineering, surveying, coastal engineering, geotechnical engineering, hydrology, environmental engineering, earthquake engineering, and structural engineering. Statistics are essential for planning experiments, collecting and analyzing data, and drawing conclusions in these diverse fields.
This document provides an overview of statistics used in business management. It defines descriptive and inferential statistics, and notes some common applications like marketing, production, finance, and accounting. Statistics is important for planning, economics, industry, science, education, and war. The document outlines limitations of statistics and provides references for further reading.
Data analysis provides insights into audience preferences that can help create successful TV shows and make good business decisions. For example, Game of Thrones analyzed audience demand for fantasy genres as well as reactions to sample episodes. Both Amazon and Netflix released sample episodes to collect data on viewer behaviors like pausing and rewatching. Netflix was more successful because in addition to data analysis, it allowed intuition and risk-taking in decision making. While data provides insights, human judgment is still needed to solve problems and make effective choices.
Sometimes it’s hard to know what statistics are worthy of trust. But we shouldn’t count out stats altogether … instead, we should learn to look behind them.
Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events
Data visualization is a technique for representing data in a graphical format to help people understand the significance of the data. It enables decision makers to see analytics visually and identify patterns. Data visualization is important as it can identify areas needing improvement, clarify factors influencing customer behavior, and help predict sales. It provides advantages like enhanced business insights, trend identification, and predictive analysis. Choosing the right visual is key to effective data visualization.
This document discusses six ways to make data more human-centered. It argues that 1) human insight is needed to frame problems and avoid costly missteps, 2) more data does not always mean better data and can increase false correlations, 3) human data is inherently biased so experience and judgment are needed, 4) context is critical to understand data but difficult for machines, 5) data can help abandon stereotypes if used properly though machines struggle with patterns, and 6) stories told with data lack human emotion which is important for marketing. The conclusion states that data should be made intelligent like the human brain which can make wise decisions based on emotions and circumstances in a way machines have not developed.
Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems.
This document discusses big data and how context is important for extracting meaningful insights from large datasets. It provides examples of big data sources like images, video, audio and text. Big data algorithms are needed to filter out noise and extract real human meaning from the data by understanding context. Predictive tools like Google Now track online behavior over time to predict what information a user might need. The document also discusses how diversity in learning can foster more creative thinking when analyzing big data. Finally, it outlines two areas where big data and creativity meet in marketing - optimization by detecting patterns in large observations, and experimentation by rapidly testing and refining multiple digital messages.
This slide is an exercise for the inquisitive students preparing for the competitive examinations of the undergraduate and postgraduate students. An attempt is being made to present the slide keeping in mind the New Education Policy (NEP). An attempt has been made to give the references of the facts at the end of the slide. If new facts are discovered in the near future, this slide will be revised.
This presentation is related to the brief History of Kashmir (Part-I) with special reference to Karkota Dynasty. In the seventh century a person named Durlabhvardhan founded the Karkot dynasty in Kashmir. He was a functionary of Baladitya, the last king of the Gonanda dynasty. This dynasty ruled Kashmir before the Karkot dynasty. He was a powerful king. Huansang tells us that in his time Taxila, Singhpur, Ursha, Punch and Rajputana were parts of the Kashmir state.
Ajanta Paintings: Study as a Source of HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
How to Configure Public Holidays & Mandatory Days in Odoo 18Celine George
In this slide, we’ll explore the steps to set up and manage Public Holidays and Mandatory Days in Odoo 18 effectively. Managing Public Holidays and Mandatory Days is essential for maintaining an organized and compliant work schedule in any organization.
How to Add Customer Note in Odoo 18 POS - Odoo SlidesCeline George
In this slide, we’ll discuss on how to add customer note in Odoo 18 POS module. Customer Notes in Odoo 18 POS allow you to add specific instructions or information related to individual order lines or the entire order.
How to Clean Your Contacts Using the Deduplication Menu in Odoo 18Celine George
In this slide, we’ll discuss on how to clean your contacts using the Deduplication Menu in Odoo 18. Maintaining a clean and organized contact database is essential for effective business operations.
How to Configure Scheduled Actions in odoo 18Celine George
Scheduled actions in Odoo 18 automate tasks by running specific operations at set intervals. These background processes help streamline workflows, such as updating data, sending reminders, or performing routine tasks, ensuring smooth and efficient system operations.
A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 817 from Texas, New Mexico, Oklahoma, and Kansas. 97 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
CURRENT CASE COUNT: 817 (As of 05/3/2025)
• Texas: 688 (+20)(62% of these cases are in Gaines County).
• New Mexico: 67 (+1 )(92.4% of the cases are from Eddy County)
• Oklahoma: 16 (+1)
• Kansas: 46 (32% of the cases are from Gray County)
HOSPITALIZATIONS: 97 (+2)
• Texas: 89 (+2) - This is 13.02% of all TX cases.
• New Mexico: 7 - This is 10.6% of all NM cases.
• Kansas: 1 - This is 2.7% of all KS cases.
DEATHS: 3
• Texas: 2 – This is 0.31% of all cases
• New Mexico: 1 – This is 1.54% of all cases
US NATIONAL CASE COUNT: 967 (Confirmed and suspected):
INTERNATIONAL SPREAD (As of 4/2/2025)
• Mexico – 865 (+58)
‒Chihuahua, Mexico: 844 (+58) cases, 3 hospitalizations, 1 fatality
• Canada: 1531 (+270) (This reflects Ontario's Outbreak, which began 11/24)
‒Ontario, Canada – 1243 (+223) cases, 84 hospitalizations.
• Europe: 6,814
Lecture 1 Introduction history and institutes of entomology_1.pptxArshad Shaikh
*Entomology* is the scientific study of insects, including their behavior, ecology, evolution, classification, and management.
Entomology continues to evolve, incorporating new technologies and approaches to understand and manage insect populations.
3. Statistics-Definition
• The practice or science of collecting and analysing numerical data in
large quantities, especially for the purpose of inferring proportions in a
whole from those in a representative sample.
4. Statistics-Importance
• It is important for researchers and also consumers of research to
understand statistics so that they can be informed, evaluate the
credibility and usefulness of information, and make appropriate
decisions.
5. 10 Awesome Reasons Where Statistics
prove to be Important:
• Weather Forecasts
• Emergency Preparedness
• Predicting Disease
• Medical Studies
• Genetics
• Political Campaigns
• Insurance
• Consumer Goods
• Quality Testing
• Stock Market
6. Why now? What's new about statistics?
• Statistics is an emergent discipline that has rapidly adapted to current
challenges..
• In today's era of big data -- where the computer and network are
everywhere and everything can be measured -- you need statistics to
make that data useful.
7. Do we really trust statistics? Different
statistics say different things.
The International year of Statistics 2013 is the occasion to remind us of the
value of:
• Statistical methods.
• Learning how to use them responsibly.
• Statistical software as the tools of analysis.
• Using statistical professionals to help us out when needed.
8. Statistics plays a vital role in every fields of human activity.
Statisticians know how to:
• Design studies
• Collect trustworthy data
• Analyse the data appropriately
• Check assumptions
• Draw reliable conclusions
9. Few potential statistical mishaps that
commonly lead to misuse:
• p-value
• Faulty Questions
• Biased Sample
• Data Fishing
• Overgeneralization
• False Causality
• Incorrect analysis choices
• Violation of the assumptions for an analysis
• Data Dredging
• Data manipulation